Guest Editorial: Data Analytics Streamlines Autonomous Driving

Artificial Intelligence (AI) incorporates the decision-making engine that is responsible for automating vehicle driving without human intervention. However, reliable and accurate decisions can only be concluded when a history of events has been accumulated by the AI engine for an extended set of operations over prolonged periods. The events are associated with status transitions of the vehicle while traveling between different geolocations. Vehicle sensors also generate sets of various information that reports platform status and the visuals of its surrounding domains including nearby objects. The automation system also acquires additional data from vehicle-to-vehicle communications and intelligent transportation systems. This diverse data helps to draw the Augmented Reality (AR) and Virtual Reality (VR) of surrounding domains that can also interact together to produce a new combined Mixed Reality (MR). Correlating all those realities with peripheral data sources leads to new 3D synergy namely eXtended Reality (XR). This aggregation of data is supported by key technology enablers such as cross-layer cyber-physical features and Bigdata storage. Training those families of labeled data improves the accuracy of machine learning predictions and safety of autonomous vehicles. This proves that acquiring more data with smart categorizing will enrich the autonomy of the transportation system.